scholarly journals dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R

2019 ◽  
Vol 88 (5) ◽  
Author(s):  
Victor Maus ◽  
Gilberto Câmara ◽  
Marius Appel ◽  
Edzer Pebesma
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Dynamic Time Warping ◽  
Satellite Image ◽  
Time Warping ◽  
Series Analysis ◽  
Dynamic Time

scholarly journals Neural-Network Time-Series Analysis of MODIS EVI for Post-Fire Vegetation Regrowth

2018 ◽  
Vol 7 (11) ◽  
pp. 420 ◽  
Author(s):  
Christos Vasilakos ◽  
George E. Tsekouras ◽  
Palaiologos Palaiologou ◽  
Kostas Kalabokidis
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Analysis ◽  
Multidimensional Scaling ◽  
Fuzzy Clustering ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
Series Analysis ◽  
Modis Evi ◽  
Dynamic Time

The time-series analysis of multi-temporal satellite data is widely used for vegetation regrowth after a wildfire event. Comparisons between pre- and post-fire conditions are the main method used to monitor ecosystem recovery. In the present study, we estimated wildfire disturbance by comparing actual post-fire time series of Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and simulated MODIS EVI based on an artificial neural network assuming no wildfire occurrence. Then, we calculated the similarity of these responses for all sampling sites by applying a dynamic time warping technique. Finally, we applied multidimensional scaling to the warping distances and an optimal fuzzy clustering to identify unique patterns in vegetation recovery. According to the results, artificial neural networks performed adequately, while dynamic time warping and the proposed multidimensional scaling along with the optimal fuzzy clustering provided consistent results regarding vegetation response. For the first two years after the wildfire, medium-high- to high-severity burnt sites were dominated by oaks at elevations greater than 200 m, and presented a clustered (predominant) response of revegetation compared to other sites.

scholarly journals Satellite Image Time Series Clustering via Time Adaptive Optimal Transport

2021 ◽  
Vol 13 (19) ◽  
pp. 3993
Author(s):  
Zheng Zhang ◽  
Ping Tang ◽  
Weixiong Zhang ◽  
Liang Tang
Keyword(s):  
Time Series ◽  
Similarity Measure ◽  
Dynamic Time Warping ◽  
Optimal Transport ◽  
Satellite Image ◽  
Time Warping ◽  
Unsupervised Analysis ◽  
Training Samples ◽  
Dynamic Time ◽  
Visual Results

Satellite Image Time Series (SITS) have become more accessible in recent years and SITS analysis has attracted increasing research interest. Given that labeled SITS training samples are time and effort consuming to acquire, clustering or unsupervised analysis methods need to be developed. Similarity measure is critical for clustering, however, currently established methods represented by Dynamic Time Warping (DTW) still exhibit several issues when coping with SITS, such as pathological alignment, sensitivity to spike noise, and limitation on capacity. In this paper, we introduce a new time series similarity measure method named time adaptive optimal transport (TAOT) to the application of SITS clustering. TAOT inherits several promising properties of optimal transport for the comparing of time series. Statistical and visual results on two real SITS datasets with two different settings demonstrate that TAOT can effectively alleviate the issues of DTW and further improve the clustering accuracy. Thus, TAOT can serve as a usable tool to explore the potential of precious SITS data.

scholarly journals A Quantum-inspired Entropic Kernel for Multiple Financial Time Series Analysis

2020 ◽  
Author(s):  
Lu Bai ◽  
Lixin Cui ◽  
Yue Wang ◽  
Yuhang Jiao ◽  
Edwin R. Hancock
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Similarity Measure ◽  
Dynamic Time Warping ◽  
Financial Time Series ◽  
Time Varying ◽  
Time Warping ◽  
Classical Dynamic ◽  
Financial Time ◽  
Dynamic Time

Network representations are powerful tools for the analysis of time-varying financial complex systems consisting of multiple co-evolving financial time series, e.g., stock prices, etc. In this work, we develop a new kernel-based similarity measure between dynamic time-varying financial networks. Our ideas is to transform each original financial network into quantum-based entropy time series and compute the similarity measure based on the classical dynamic time warping framework associated with the entropy time series. The proposed method bridges the gap between graph kernels and the classical dynamic time warping framework for multiple financial time series analysis. Experiments on time-varying networks abstracted from financial time series of New York Stock Exchange (NYSE) database demonstrate that our approach can effectively discriminate the abrupt structural changes in terms of the extreme financial events.

scholarly journals DYNAMIC TIME WARPING FOR CROPS MAPPING

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 947-951
Author(s):  
M. Belgiu ◽  
Y. Zhou ◽  
M. Marshall ◽  
A. Stein
Keyword(s):  
Time Series ◽  
Dynamic Time Warping ◽  
Satellite Image ◽  
Time Warping ◽  
Training Samples ◽  
Good Classification ◽  
Dynamic Time ◽  
Temporal Sequences ◽  
Better Than

Abstract. Dynamic Time Warping (DTW) has been successfully used for crops mapping due to its capability to achieve good classification results when a reduced number of training samples and irregular satellite image time series is available. Despite its recognized advantages, DTW does not account for the duration and seasonality of crops and local differences when assessing the similarity between two temporal sequences. In this study, we implemented a Weighted Derivative modification of DTW (WDDTW) and compared it with DTW and Time Weighted Dynamic Time Warping (TWDTW) for crops mapping. We show that WDDTW outperformed DTW achieving an overall accuracy of 67 %, whereas DTW obtained an accuracy of 57%. Yet, TWDTW performed better than both methods obtaining an accuracy of 88%.

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